capacity planning

The per-token sticker price hides the real decision. Self-hosting only wins past a volume break-even most teams misjudge. Here is the back-of-envelope a staff engineer does on the whiteboard.

At a billion vectors the index choice is a memory budget problem before it's a recall problem. Flat search is out, HNSW may not fit, and PQ trades recall for RAM. Here is the decision a staff engineer makes on the whiteboard.

Back-of-the-envelope for LLM serving, done properly. The arithmetic is four numbers deep and it usually kills the plan it was asked to justify, which is exactly why interviewers ask it.

GPUs are scarce and expensive, so multi-region HA for LLMs is not just web-app HA with bigger boxes. Capacity, routing, and failover all bend around the GPU constraint. Here is the design.

Launching an LLM service on a vibes-based GPU count is how you get paged on day one. Capacity planning for LLMs differs from web services because tokens, not requests, are the unit. Here is how to size it.

When every request thinks for 30,000 tokens, serving flips from compute-bound prefill to memory-bound decode, and the KV cache becomes the resource you actually schedule. The levers that ruled chat traffic stop being the ones that matter.